Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes
Abstract
While exploring human mobility can benefit many applications such as smart transportation, city planning, and urban economics, there are two key questions that need to be answered: (i) What is the nature of the spatial diffusion of human mobility across regions with different urban functions? (ii) How to spot and trace the trip purposes of human mobility trajectories? To answer these questions, we study large-scale and city-wide taxi trajectories; and furtherly organize them as arrival sequences according to the chronological arrival time. We figure out an important property across different regions from the arrival sequences, namely human mobility synchronization effect, which can be exploited to explain the phenomenon that two regions have similar arrival patterns in particular time periods if they share similar urban functions. In addition, the arrival sequences are mixed by arrival events with distinct trip purposes, which can be revealed by the regional environment of both the origins and destinations. To that end, in this paper, we develop a joint model that integrates Mixture of Hawkes Process (MHP) with a hierarchical topic model to capture the arrival sequences with mixed trip purposes. Essentially, the human mobility synchronization effect is encoded as a synchronization rate in the MHP; while the regional environment is modeled by introducing latent Trip Purpose and POI Topic to generate the Point of Interests (POIs) in the regions. Moreover, we provide an effective inference algorithm for parameter learning. Finally, we conduct intensive experiments on synthetic data and real-world data, and the experimental results have demonstrated the effectiveness of the proposed model.
Recommended Citation
P. Wang et al., "Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes," Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017, Halifax, Canada), pp. 495 - 503, Association for Computing Machinery (ACM), Aug 2017.
The definitive version is available at https://doi.org/10.1145/3097983.3098067
Meeting Name
23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 (2017: Aug. 13-17, Halifax, Canada)
Department(s)
Computer Science
Keywords and Phrases
Data mining; Economics; Inference engines; Mixtures; Smart city; Synchronization; Taxicabs; Hierarchical topic models; Human mobility; Inference algorithm; Regional environments; Spatial diffusions; Synchronization rate; Trip purpose; Variational inference; Urban transportation; Hawkes process
International Standard Book Number (ISBN)
978-1-4503-4887-4
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 Aug 2017
Comments
This research was partially supported by University of Missouri Research Board (proposal number: 4991). This work was also partially supported by the National Key Research Program of China under Grant No. 2016YFB1000600, No. 2016YFB0501900 and Natural Science Foundation of China under Grant No. 61402435.